没有匹配项时将打印空数据框
smer_prods是一本字典，其每个键都包含一个列表作为值：
smer_prods = {
'ragi vermicelli' : ['ragi vermicelli'],
'rice vermicelli' : ['rice vermicelli'],
'vermicelli jupiter' : ['vermicelli jupiter'],
'lemon & tamarind vermicelli' : ['lemon & tamarind vermicelli'],
'finosta vermicelli' : ['finosta vermicelli-5kg'],
'rosted vermicelli' : ['roasted vermicelli'],
'semiya/vermicelli' : ['semiya / vermicelli 900grams'],
'red chili' : ['red chilli (lal mirch)','guntur red chilli','red chilly whole(lal mirch)', 'red chilly wg', 'red chilli whole (hot) 1 kg', 'red chilli whole (rich colour) 1 kg'],
'red chili powder' : ['red chilli fresh-kg','red chilli powder (rich colour) 1 kg','red chilli powder (hot) 1 kg','red chilli powder','lal mirch powder','lal mirch powder 100gms', 'lal mirch powder 1kg', 'lal mirch powder 200gms', 'lal mirch powder 500gms'],
'red chilli sauce' : ['red chilli sauce', 'red chilli sauce 200gm pet bottle 48X200gm', 'hot chili sauce'],
'sriraja hot chilli sauce' : ['sriraja hot chilli sauce', 'sriracha hot chilli sauce'],
'mineral water' : ['himalayan orchard pure peach flavoured natural mineral water - 500 ml','himalayan orchard pure strawberry flavoured natural mineral water - 500 ml','himalayan orchard pure apple flavoured natural mineral water - 500 ml','himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle'],
}
对于字典中的每个键，我们都在遍历其列表值。如果列表中的任何值与pandas数据框中的ITEM NAME匹配，则应在字典中同时打印数据框和列表值。发生匹配时，它可以完美工作。 此外，如果没有匹配项，它将打印一个空的数据框。我想防止这种情况。如果没有匹配项，则无需在数据帧df1中存储空行。
df = pd.read_csv('toy_data.csv', engine='python')
#print(df)
for x in smer_prods:
list_smer = smer_prods[x]
#print(list_smer)
for y in list_smer:
mask = df['ITEM NAME'] == y
df1 = df[mask]
print('-'*80)
print(df1)
print('-'*80)
print(list_smer)
输出：
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['vermicelli jupiter']
--------------------------------------------------------------------------------
S.NO ITEM NAME
1 2 sriraja hot chilli sauce
--------------------------------------------------------------------------------
['sriraja hot chilli sauce', 'sriracha hot chilli sauce']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['sriraja hot chilli sauce', 'sriracha hot chilli sauce']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
S.NO ITEM NAME
8 9 himalayan orchard pure strawberry flavoured na...
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['himalayan orchard pure peach flavoured natural mineral water - 500 ml', 'himalayan orchard pure strawberry flavoured natural mineral water - 500 ml', 'himalayan orchard pure apple flavoured natural mineral water - 500 ml', 'himalayan - the natural mineral water - 500ml bottle', 'himalayan - the natural mineral water - 200ml bottle', 'himalayan - the natural mineral water - 1ltr bottle']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['vermicelli upma']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['roasted vermicelli']
--------------------------------------------------------------------------------
S.NO ITEM NAME
4 5 rice vermicelli
--------------------------------------------------------------------------------
['rice vermicelli']
--------------------------------------------------------------------------------
S.NO ITEM NAME
0 1 finosta vermicelli-5kg
--------------------------------------------------------------------------------
['finosta vermicelli-5kg']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['red chilli sauce', 'red chilli sauce 200gm pet bottle 48X200gm', 'hot chili sauce']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['red chilli sauce', 'red chilli sauce 200gm pet bottle 48X200gm', 'hot chili sauce']
--------------------------------------------------------------------------------
S.NO ITEM NAME
2 3 hot chili sauce
--------------------------------------------------------------------------------
['red chilli sauce', 'red chilli sauce 200gm pet bottle 48X200gm', 'hot chili sauce']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['lemon & tamarind vermicelli']
--------------------------------------------------------------------------------
Empty DataFrame
Columns: [S.NO, ITEM NAME]
Index: []
--------------------------------------------------------------------------------
['red chilli (lal mirch)', 'guntur red chilli', 'red chilly whole(lal mirch)', 'red chilly wg', 'red chilli whole (hot) 1 kg', 'red chilli whole (rich colour) 1 kg']
--------------------------------------------------------------------------------
S.NO ITEM NAME
3 4 guntur red chilli
--------------------------------------------------------------------------------
['red chilli (lal mirch)', 'guntur red chilli', 'red chilly whole(lal mirch)', 'red chilly wg', 'red chilli whole (hot) 1 kg', 'red chilli whole (rich colour) 1 kg']
------------------------------------------------------------------------------
csv for pandas数据框如下所示：
1 个答案:
答案 0 :(得分：1)
使用.any（）
for x in smer_prods:
list_smer = smer_prods[x]
#print(list_smer)
for y in list_smer:
mask = df['ITEM NAME'] == y
if mask.any() == True:
df1 = df[mask]
print('-'*80)
print(df1)
print('-'*80)
print(list_smer)
示例：
mask = [False, True, False]
print(any(mask))
mask = [False, False, False]
print(any(mask))
输出：
True
False
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